Big Data: 
Como utilizar dados 
para tomar decisões 
mais inteligentes 
Rodolfo Ohl 
Country Manager 
SurveyMonkey
2 
Ajudar as pessoas a tomarem melhores decisões
Nós ajudamos as pessoas a obterem insights 
clientes criam 
pesquisas 
distribuem para 
outros 
as pessoas 
respondem 
a p...
Avaliação de 
Desempenho 
Pesquisa 
Acadêmica 
TCC, Mestrado etc. 
Pesquisa 
de Clima 
Planejamento 
de Eventos 
Testes 
e...
Pessoal 1% 
ONGs 
27% 
Educação 
21% 
Gov. 
9% 
Empresas 
42% 
99% 
Das empresas da Fortune 500 
Nossos clientes 
Mais de ...
80% 
dos clientes optam 
pelos planos anuais 
$24 
Opção de plano 
mensal também 
disponível 
Modelo de Negócios Freemium ...
A nossa trajetória 
Um caminho não convencional para o Vale do Silício 
Mudou-se para Palo Alto 
e contratou equipe para 
...
Quão familiar você está 
com o termo “Big Data”?
BIG DATA está mudando o mundo 
Mas nem todos os dados são criados de forma igual.
Dados Implícitos
Academia $272 
Clareamento dentário $88 
Hotel $210 
How “Big Data” Can Predict Your Divorce, ABC News/Nightline, 
Decembe...
Mudança da Marca da Cerveja = Divórcio
Análise Preditiva é um novo e crescente campo de atuação.
Quão confiante você está em relação a ter 
todas as qualificações ou capacitação 
necessária para competir em uma economia...
Em geral, quão preparado você pensa 
que a sua empresa está para lidar com 
SurveyMonkey, Brazilians & Big Data, March 201...
80% acreditam que suas empresas 
Aumentaram investimentos nestas 3 áreas 
Contratação/desenvolvimento ferramentas 
relacio...
O modelo preditivo de gravidez 
How Companies Learn Your Secrets, The New York Times Magazine, February 
2012
Saída 
Voz
Dados Explícitos
Demorado 
Alto Investimento 
Não escalável 
Primórdios
Dados explícitos em escala
Em um dia
Vendas de SUV estavam 
estáveis em 2007 
“Vendas da maioria do carros 
utilitários grandes de luxo estão 
em alta em 2007,...
O que você está procurando 
num novo carro? 
Market Research Driving Product Development at Ford Motor Company
Job exit by women 
The Happiness Machine, Slate, January 2013
Qual foi a principal razão 
por ter saído da empresa? 
The Happiness Machine, Slate, January 2013
32 
Implícito + Explícito
Implícito + Explícito 
Análise de funil 
Pesquisa sobre abandono de carrinho
Implícito + Explícito 
Pesquisa com visitantes 
Taxa de conversão por categoria
Alguns exemplos de pequisas 
para e-commerce 
- Pesquisa para validação de hipóteses 
- Pesquisa de satisfação de clientes...
36 
Geração, verificação e balanceamento de insights 
Dados implícitos verificados por dados explícitos = extremamente val...
Rodolfo Ohl: rodolfo@surveymonkey.com 
www.surveymonkey.com.br 
br.blog.surveymonkey.com 
Facebook.com/surveymonkeybrasil ...
Como utilizar os dados que você tem nas mãos para conhecer melhor os seus clientes.
Como utilizar os dados que você tem nas mãos para conhecer melhor os seus clientes.
Como utilizar os dados que você tem nas mãos para conhecer melhor os seus clientes.
Como utilizar os dados que você tem nas mãos para conhecer melhor os seus clientes.
Como utilizar os dados que você tem nas mãos para conhecer melhor os seus clientes.
Como utilizar os dados que você tem nas mãos para conhecer melhor os seus clientes.
Como utilizar os dados que você tem nas mãos para conhecer melhor os seus clientes.
Como utilizar os dados que você tem nas mãos para conhecer melhor os seus clientes.
Como utilizar os dados que você tem nas mãos para conhecer melhor os seus clientes.
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Como utilizar os dados que você tem nas mãos para conhecer melhor os seus clientes.

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Slides da palestra "Como utilizar os dados que você tem nas mãos para conhecer melhor os seus clientes" feita pelo Rodolfo Ohl, Country Manager Brasil da SurveyMonkey.

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  • Big Data. It’s the buzz word of the moment. Everyone is talking about it
    Big Data went from an emergent idea to a Holy-Grail solution in minutes - record time - promising everything from better use of medical records to the smarter planet we’ve been hearing about in countless TV ads
    However, it seems that while the term is quite popular [CLICK]


  • 1/3 of Brazilians don’t know what it means
    This data is from a study we commissioned to examine the use and understanding of Big Data by Brazilian professionals. We surveyed over 1,000 business people 18-64 about 2 months ago and found out some interesting things
    Nearly four in 10 (36) Brazilian worker are “not too” or “not at all” familiar with the term. And some of those who say they know the term actually misidentify it as a “big day."
     
  • GOOG receives over 2M search queries a minute
    Consumers spend over a quarter of a million dollars online EVERY MIN
    Twitter users send over 100K tweets
    At SurveyMonkey, we receive 50MB of new data from customers

    The Internet has become a place where massive amounts of information and data are being generated every day. This is big data

    Big data isn’t just some abstract concept created by the IT crowd, but a continually growing stream of digital activity pulsating through cables and airwaves across the world

    Every minute giant amounts of it are being generated from every phone, website and application across the Internet
    In the last two years, humans have created 90% of all information ever created by our species. Overwhelming.
    There is too much data of too many different types. One doesn’t know where to start. This is big data

    So, where to do we start? Let’s break it down

    Big Data. It’s the buzz word of the moment. Everyone is talking about it
    Big Data went from an emergent idea to a Holy-Grail solution in minutes - record time - promising everything from better use of medical records to the smarter planet we’ve been hearing about in countless TV ads
    However, it seems that while the term is quite popular [CLICK]

  • Implicit data is gleaned or implied
    That’s why Big Data carries a darker connotation, as it’s linguistic cousin - Big Brother, Big Oil and Big Government.
    Some people find big data kinda creepy. For example…
  • Another sign of divorce – according to another study? A switch in beer brands

    So watch what your partner is drinking!
  • Every single piece of data available is being crunched
    Predictive analytics is the hot, new job

    Almost every major retailer, from grocery chains to investment banks to the U.S. Postal Service, has a “predictive analytics” department devoted to understanding not just consumers’ shopping habits, but also their personal habits, so as to more efficiently market to them
  • And, Brazilians say they’re ready

    -Most BR workers (64) express confidence that they have the education and skills needed to compete in an increasingly data-driven economy — with 22 pct saying they are “extremely confident.”

    That’s good news. Brazilian companies are going to need the help, because [CLICK]


  • NOTE: this is among those who are familiar with the term big data

    Nearly one in four – almost a quarter – of employees say their companies are ill-prepared to handle the trend.
    But, they are planning to invest
     

  • About eight in 10 (80) expect their companies make increased investments in three key areas:

    purchase or build software or tools (53 US);
    to hire additional employees, (79, vs. 55 US); and
    collect new kinds of customer data (US 60)

    This is a larger investment than what’s planned in the US where large companies are already using data to drive all different types of decisions
  • Large companies like Target, for example, use big data analytics to determine when its customers are at a time in their lives when they may be inclined to alter their shopping habits and preferences

    And among life events, none are more important than the arrival of a baby
    At that moment, new parents’ habits are more flexible than at almost any other time in their adult lives. If companies can identify pregnant shoppers, they can earn millions

    Target crawled their database and identified 25 products that, when analyzed together, allowed them to assign each shopper a “pregnancy prediction” score

    AND it could estimate her due date! So Target could send coupons timed to very specific stages of her pregnancy
  • Lots of people buy lotion, but Target noticed that women on the baby registry were buying larger quantities of unscented lotion around the beginning of their second trimester [CLICK]
     
    VISUAL: PREGNANCY PREDICTION SCORE + VITAMINS (10)
    Another analyst noted that sometime in the first 20 weeks, pregnant women loaded up on supplements like calcium, magnesium and zinc [CLICK]
     
    VISUAL: PREGNANCY PREDICTION SCORE + VITAMINS + SOAP AND COTTON BALLS (10)
    Many shoppers purchase soap and cotton balls, but when someone suddenly starts buying lots of scent-free soap and extra-big bags of cotton balls, in addition to hand sanitizers and washcloths, it signals they could be getting close to their delivery date

    This is all well and good, till it goes horribly wrong
  • About a year into the pregnancy prediction program, a man walked into a Target outside Minneapolis and demanded to see the manager. He was clutching coupons that had been sent to his daughter, and he was angry, according to an employee who participated in the conversation
    “My daughter got this in the mail!” he said. “She’s still in high school, and you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?”

    The manager didn’t have any idea what the man was talking about. He looked at the mailer. Sure enough, it was addressed to the man’s daughter and contained advertisements for maternity clothing, nursery furniture and pictures of smiling infants. The manager apologized and then called a few days later to apologize again

    On the phone, though, the father was somewhat abashed. “I had a talk with my daughter,” he said. “It turns out there’s been some activities in my house I haven’t been completely aware of. She’s due in August. I owe you an apology.”

    That’s the power of implicit data. However, there is an inherent creepiness involved in this type of data collection and analysis.
    Should Target know a girl is pregnant before her father?

    And, with implicit data, you never know if your predictions are correct
  • Implicit data is derived from observing and analyzing your and others’ behavior
    It’s largely based on a passive collection of people’s behaviors or habits. But it’s inherently flawed

    Amazon can’t tell the difference between when Anne, a grandmother, was shopping for a birthday gift for her grandson and her regular online purchases
    Amazon doesn’t know when you’re buying a book for work, or a gift for a friend. So what happens is your recommendations get polluted, and never truly reflect what you are interested in purchasing
  • Prof Eagle is currently working with mobile carriers, governments, developmental organizations, and public health and welfare agencies around the world to mine cell phone records for meaningful data that can be used to solve some of the world’s biggest social problems

    His team looks at how people move around – thinking of people as particles
    Like particle, people tend to move around within predictable boundaries/patters. When a person's radius changes, it means little. BUT, if a whole village or region changes their movement, something big has happened

    In Rwanda, Eagle tried to correlate the sudden changes of movements with cholera outbreaks. In one village, Eagle and his researchers suddenly noticed a sudden shrinking of the movement ranges of its residents
    They thought they had predicted a cholera outbreak, BUT what they had actually detected was a flood caused by a broken dam, which had washed out local roads, greatly constraining the local population's movement.
    Should've just called one of those mobile phones and asked what was going on!

    Just ask. What a novel concept! Which leads us to explicit data
  • A great book called Exit, Voice and Loyalty by Albert Hirshman. It gives us a framework in which to think about implicit and explicit data and the benefits of soliciting opinions from your most loyal customers. Hirshman examines decline, rather than growth, and finds that there are two basic reactions to decline: [CLICK]
     
    VISUAL: BOOK COVER + (bubble) EXIT/VOICE (28)
    Exit explain
    Voice explain
    And, the difference between how people choose between exit and voice is LOYALTY
    Loyalty is seen in the function of retarding exit and of permitting voice to play its proper role.
    The book points out that typically, people and business rely on exit – we’d call it implicit – data to identify an issue, but can rarely, on its own, counteract decline
    Declining sales #s, for example
    But by that time, it might be too late. Exit is a lagging indicator
    Voice is your canary in a coal mine. Explicit data gives Big Data a voice

  • Explicit Data is just that – explicit. It’s fully revealed or expressed without vagueness or ambiguity
    Leaving no question as to meaning or intent

    Explicit data are insights taken directly from the individual. It is inherently more reliable
    And, it removes the creepiness factor from the equation

    So, why wouldn’t we use explicit data all the time?
  • Well, historically, explicit data is slow and expensive. And, not available at scale. It’s easy to view explicit data as a dinosaur
    That’s why people – traditionally – only think of Big Data as implicit data

    However, technology has changed that
  • The internet allows people to gather explicit data at scale. The ability to provide explicit data through a variety of platforms is empowering for consumers and enlighting for businesses

    Whenever you fill out a survey, rate a business, like a brand, review a service or school, you’re contributing to explicit data
  • Just to give you an idea of the type of scale we’re talking about, at SurveyMonkey, in a day we receive:
    70GB of new data
    2M survey responses
    20M questions answered

    Daily! That’s big data. Explicit data at scale

    And, when you can get quality, explicit data at scale, it becomes incredibly valuable. And, can tell us things that implicit data, on its own, can’t
  • Pre-financial crisis sales data pointed to continued success of large SUV sales

    However, Ford had the foresight to make a bet on investing in smaller, more fuel efficient cars. How?
  • Ford directly gathered consumer feedback and used this data set IN ADDITION to sales data to develop future product design
    This move was credited with saving them from having to take a government bailout money

    Both implicit and explicit data are needed and work well together. They work as a check and balance against each other
    If you’re looking at data in silos, or at just one of these data sets, you may identify a trend, but you will never solve a problem

    Google had a problem
  • Google has “a sophisticated employee-data tracking program where they gain empirical certainty about every aspect of Google’s workers’ lives.”
    The team was analyzing the data generated from Google’s 15K employees and found that they were loosing one group of employees faster than any other. Women

    The attrition rate for women was much higher than the rest of the employees. The team then surveyed female employees and realized, they didn’t have a “woman” problem. They had a “new mom” problem
  • Survey results showed that new mothers found the company’s standard 12-week maternity unsatisfactory
    And, Google’s 5-month maternity leave program was born. Google was able to better retain its female workers, and improve their happiness at the workplace
  • At SurveyMonkey, we use both implicit and explicit data to run and optimize our business.
    The implicit data we analyze includes:
    # of questions asked
    # of daily surveys
    SEM terms
    Free to pd conversion
    Pricing packages
    Churn

    And, we couple this data with surveys:
    Customer satisfaction
    Cancellation
    Product feedback
    For example, looking at our churn numbers only gets us so far in understanding why our customers leave

    The majority of our customers leave because they don’t need a survey right now, but tell us that they plan to come back when they do. This helps us better predict our business
  • Gut feel and intuition are being replaced by data every day, but just using one type of data is dangerous
    Check implicit with explicit. The feedback loop between the insights of each is most critical
    Analyze your data but make sure to ask why
    Implicit is the what

    Explicit is the why. And, I would argue, the more important data set

    It’s dangerous to not ask why. Look at RIM

  • Now, the last chapter of this story hasn’t been written, so we’ll see

    But, sales have declined so much that it’s virtually impossible for them to recover
    RIM consistently ignored Voice. People LOVE our email and will stay with us
    A few customers asked for apps; They don’t need apps
    RIM executives needed convincing a color screen was necessary! Who needs to read their email in color, they stated?!

    Explicit data is the voice of your most loyal customers. And, more valuable than implicit data. We need to not only listen to it, but encourage it
  • Encourage your customers AND employees to give you feedback
    Make it easier for them to tell you what they think
    Issues like privacy and creepiness disappear when they’re engaged in the conversation
    People feel empowered and loyal when they are recognized and have a voice.
    In turn, your Big Data will find it’s voice
  • Como utilizar os dados que você tem nas mãos para conhecer melhor os seus clientes.

    1. 1. Big Data: Como utilizar dados para tomar decisões mais inteligentes Rodolfo Ohl Country Manager SurveyMonkey
    2. 2. 2 Ajudar as pessoas a tomarem melhores decisões
    3. 3. Nós ajudamos as pessoas a obterem insights clientes criam pesquisas distribuem para outros as pessoas respondem a pequisa clientes analisam e obtém insights
    4. 4. Avaliação de Desempenho Pesquisa Acadêmica TCC, Mestrado etc. Pesquisa de Clima Planejamento de Eventos Testes e Quizzes Avaliação de Treinamento Pesquisa de Clima Questionários para ONGs Pesquis a sobre Produto Pesquisa com pais Pesquisa de Satisfação de Clientes Nossos clientes são criativos
    5. 5. Pessoal 1% ONGs 27% Educação 21% Gov. 9% Empresas 42% 99% Das empresas da Fortune 500 Nossos clientes Mais de 20 milhões de usuários
    6. 6. 80% dos clientes optam pelos planos anuais $24 Opção de plano mensal também disponível Modelo de Negócios Freemium Clientes fazem upgrade para planos premium para adicionar funcionalidades
    7. 7. A nossa trajetória Um caminho não convencional para o Vale do Silício Mudou-se para Palo Alto e contratou equipe para crescer Lucrativa desde o 1º dia 1999 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Criada neste apartamento
    8. 8. Quão familiar você está com o termo “Big Data”?
    9. 9. BIG DATA está mudando o mundo Mas nem todos os dados são criados de forma igual.
    10. 10. Dados Implícitos
    11. 11. Academia $272 Clareamento dentário $88 Hotel $210 How “Big Data” Can Predict Your Divorce, ABC News/Nightline, December 2012
    12. 12. Mudança da Marca da Cerveja = Divórcio
    13. 13. Análise Preditiva é um novo e crescente campo de atuação.
    14. 14. Quão confiante você está em relação a ter todas as qualificações ou capacitação necessária para competir em uma economia cada vez mais orientada a dados? SurveyMonkey, Brazilians & Big Data, Março 2014
    15. 15. Em geral, quão preparado você pensa que a sua empresa está para lidar com SurveyMonkey, Brazilians & Big Data, March 2014 “Big Data”?
    16. 16. 80% acreditam que suas empresas Aumentaram investimentos nestas 3 áreas Contratação/desenvolvimento ferramentas relacionadas a base de dados Contratação de funcionários adcionais Coleta/acesso a novos tipos de dados SurveyMonkey, Brazilians & Big Data, Março 2014
    17. 17. O modelo preditivo de gravidez How Companies Learn Your Secrets, The New York Times Magazine, February 2012
    18. 18. Saída Voz
    19. 19. Dados Explícitos
    20. 20. Demorado Alto Investimento Não escalável Primórdios
    21. 21. Dados explícitos em escala
    22. 22. Em um dia
    23. 23. Vendas de SUV estavam estáveis em 2007 “Vendas da maioria do carros utilitários grandes de luxo estão em alta em 2007, como Land Rover, Range Rover, além de outras (grandes e caras) SUVs de marcas não luxuosas como Chevrolet Suburban” Fonte: AutoData 2007
    24. 24. O que você está procurando num novo carro? Market Research Driving Product Development at Ford Motor Company
    25. 25. Job exit by women The Happiness Machine, Slate, January 2013
    26. 26. Qual foi a principal razão por ter saído da empresa? The Happiness Machine, Slate, January 2013
    27. 27. 32 Implícito + Explícito
    28. 28. Implícito + Explícito Análise de funil Pesquisa sobre abandono de carrinho
    29. 29. Implícito + Explícito Pesquisa com visitantes Taxa de conversão por categoria
    30. 30. Alguns exemplos de pequisas para e-commerce - Pesquisa para validação de hipóteses - Pesquisa de satisfação de clientes - Pesquisa de usabilidade - Pesquisa de intenção de compras - Pesquisa com ex-clientes - Pesquisa com potenciais clientes - Pesquisa com assinantes do newletter - Pesquisa de mercado - Pesquisa para definição de nome, logo, campanha, identidade visual etc - As possibilidades são ilimitadas....
    31. 31. 36 Geração, verificação e balanceamento de insights Dados implícitos verificados por dados explícitos = extremamente valiosos Pesquisa Dados Explícitos Ação Dados Implícitos
    32. 32. Rodolfo Ohl: rodolfo@surveymonkey.com www.surveymonkey.com.br br.blog.surveymonkey.com Facebook.com/surveymonkeybrasil twitter.com/surveymonkeybr plus.google.com/+SurveymonkeyBr

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